Merge pull request 'shestakova_maria_lab_3 is ready' (#222) from shestakova_maria_lab_3 into main
Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/222
This commit is contained in:
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64d87ef5f7
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shestakova_maria_lab_3/3.1.png
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shestakova_maria_lab_3/README.md
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shestakova_maria_lab_3/README.md
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### Задание:
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Часть 1. По данным о пассажирах Титаника решите задачу классификации (с помощью дерева решений), в которой по различным характеристикам пассажиров требуется найти у выживших пассажиров два наиболее важных признака из трех рассматриваемых: Pclass, Parch, Fare
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Часть 2. Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы искусственного интеллекта»на 99% ваших данных: зависимость качества сна (Quality of Sleep) от возраста (Age) и пола (Gender). Проверьте работу модели на оставшемся проценте, сделайте вывод
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### Технологии:
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Библиотека Scikit-learn, библиотека numpy, библиотека pandas
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### Что делает лабораторная:
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Часть 1. Из выборки отбирается 3 необходимых по заданию признака, определяется целевая переменная по заданию, обучается дерево, выводятся важности признаков по каждому классу
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Часть 2. Из выборки отбирается 2 необходимых по заданию признака, определяется целевая переменная по заданию, данные разделяются на обущающую и тестовую выборку, дерево обучается классификацией и регрессией, выводятся важности признаков, предсказания значений на тестовой выборке и оценка качества
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### Как запустить:
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Первая часть лабораторной работы запускается в файле `shestakova_maria_lab_3.1.py` через Run: появляется вывод в консоли
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Вторая часть лабораторной работы запускается в файле `shestakova_maria_lab_3.2.py` через Run: появляется вывод в консоли
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### Вывод:
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Часть 1.
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Часть 2.
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По выводу можно заметить, что модель дерева классификации подходит больше для решения данной задачи
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22
shestakova_maria_lab_3/shestakova_maria_lab_3.1.py
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shestakova_maria_lab_3/shestakova_maria_lab_3.1.py
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import pandas
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from sklearn.tree import DecisionTreeClassifier
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import numpy as np
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# загрузка данных из набора
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data = pandas.read_csv('titanic.csv', index_col='Passengerid')
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data = data.loc[(np.isnan(data['Pclass']) == False) & (np.isnan(data['Parch']) == False) & (np.isnan(data['Fare']) == False) & (np.isnan(data['2urvived']) == False)]
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# отбор по заданию
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correct = data[['Pclass', 'Parch', 'Fare']]
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print(correct.head())
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y = data['2urvived']
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# дерево решений
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clf = DecisionTreeClassifier(random_state=27)
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clf.fit(correct, y)
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# важность признаков
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important = clf.feature_importances_
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print(important)
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51
shestakova_maria_lab_3/shestakova_maria_lab_3.2.py
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shestakova_maria_lab_3/shestakova_maria_lab_3.2.py
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
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from sklearn.metrics import mean_squared_error
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from sklearn.metrics import accuracy_score
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# загрузка данных из набора
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data = pd.read_csv('sleep.csv', index_col='Person ID')
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# приведение строковых ячеек к числу
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data['Gender'] = data['Gender'].map({'Male': 0, 'Female': 1})
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# признаки (X) и целевая переменная (y)
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X = data[['Age', 'Gender']]
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print(X.head())
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y = data['Quality of Sleep']
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# обуч и тест выборка
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=27)
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# дерево регрессии
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regression_tree = DecisionTreeRegressor()
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regression_tree.fit(X_train, y_train)
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test_score_reg = regression_tree.score(X_test, y_test)
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# важность признаков
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important_reg = regression_tree.feature_importances_
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# предсказание
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y_pred_reg = regression_tree.predict(X_test)
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# оценка модели
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mse = mean_squared_error(y_test, y_pred_reg)
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# дерево классификации
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classifier_tree = DecisionTreeClassifier()
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classifier_tree.fit(X_train, y_train)
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test_score_class = classifier_tree.score(X_test, y_test)
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# важность признаков
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important_class = classifier_tree.feature_importances_
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# предсказание
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y_pred_class = classifier_tree.predict(X_test)
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# оценка модели
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accuracy = accuracy_score(y_test, y_pred_class)
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print("Regression Tree:")
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print("Score:", test_score_reg)
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print("Importance:", important_reg)
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print("Mean Squared Error: {:.2f}".format(mse))
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print("")
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print("Classification Tree:")
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print("Score:", test_score_class)
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print("Importance:", important_class)
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print("Accuracy: {:.2f}%".format(accuracy * 100))
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shestakova_maria_lab_3/sleep.csv
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shestakova_maria_lab_3/sleep.csv
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Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder
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1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,None
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2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None
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3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None
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4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea
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5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea
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6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia
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7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia
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8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
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9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
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10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
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11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None
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12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
|
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13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None
|
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14,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
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15,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
|
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16,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
|
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17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea
|
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18,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,Sleep Apnea
|
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19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia
|
||||
20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
|
||||
21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
||||
22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
||||
23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
||||
24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
||||
25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
|
||||
26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
|
||||
27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
|
||||
28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
|
||||
29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
|
||||
30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
|
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31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea
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32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia
|
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33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,None
|
||||
34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
|
||||
35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
||||
36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
|
||||
37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
|
||||
38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
|
||||
39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
|
||||
40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
|
||||
41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
||||
42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
||||
43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
||||
44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
|
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45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
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46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
|
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47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
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48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
|
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49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
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50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea
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51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None
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52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None
|
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53,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
|
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55,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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56,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
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58,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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59,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
|
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61,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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62,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
|
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64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
|
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65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
|
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66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
|
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67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,None
|
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68,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,Insomnia
|
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69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None
|
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70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None
|
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71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
|
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72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
|
||||
73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
|
||||
74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
|
||||
75,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
||||
76,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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77,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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78,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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79,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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80,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
|
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81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea
|
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82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea
|
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83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None
|
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84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None
|
||||
85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None
|
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86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None
|
||||
88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None
|
||||
89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
|
||||
90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
|
||||
91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
|
||||
92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
|
||||
93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None
|
||||
94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea
|
||||
95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia
|
||||
96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea
|
||||
105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea
|
||||
106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia
|
||||
107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,None
|
||||
108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None
|
||||
109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None
|
||||
110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
|
||||
111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
|
||||
113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
|
||||
115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
|
||||
126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,None
|
||||
127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
|
||||
128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
|
||||
130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
|
||||
131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
|
||||
133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
|
||||
134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
|
||||
136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
|
||||
137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
|
||||
139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
|
||||
141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
|
||||
143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
|
||||
145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea
|
||||
146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea
|
||||
147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia
|
||||
148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia
|
||||
149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,None
|
||||
150,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None
|
||||
151,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None
|
||||
152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
|
||||
153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
|
||||
154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
|
||||
155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
|
||||
156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
|
||||
157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
|
||||
158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
|
||||
159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
|
||||
160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
|
||||
161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
|
||||
162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None
|
||||
163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None
|
||||
164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None
|
||||
165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None
|
||||
166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia
|
||||
167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,None
|
||||
168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None
|
||||
169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None
|
||||
170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
|
||||
171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
|
||||
172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
|
||||
173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
|
||||
174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
|
||||
175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
|
||||
176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
|
||||
177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
|
||||
178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea
|
||||
186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea
|
||||
187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
188,Male,43,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
189,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
190,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
191,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
192,Male,43,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
193,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
194,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
195,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
196,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
197,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
198,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
199,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia
|
||||
203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia
|
||||
204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,None
|
||||
205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,None
|
||||
206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
|
||||
207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
|
||||
208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
|
||||
209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
|
||||
210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
|
||||
212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
|
||||
219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea
|
||||
220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea
|
||||
221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
223,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
224,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
225,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
226,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
227,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
228,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
229,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
230,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
231,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
232,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
233,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
234,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
235,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
236,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
237,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
238,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
239,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
240,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
241,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
242,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
243,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
244,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia
|
||||
249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,None
|
||||
250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,None
|
||||
251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia
|
||||
252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia
|
||||
253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
254,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
255,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
256,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
257,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
258,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
|
||||
262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None
|
||||
263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None
|
||||
264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,None
|
||||
265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia
|
||||
266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia
|
||||
268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,None
|
||||
269,Female,49,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
272,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
273,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
274,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
275,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea
|
||||
278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea
|
||||
279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia
|
||||
280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
|
||||
281,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,None
|
||||
282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
283,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
284,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
285,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
286,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
287,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
288,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
289,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
290,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
291,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
292,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
293,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
294,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
295,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
296,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,None
|
||||
304,Female,51,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
|
||||
307,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
308,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
309,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
|
||||
313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia
|
||||
317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
|
||||
326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
|
||||
328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
|
||||
330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
|
||||
339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
|
||||
340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea
|
||||
341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea
|
||||
342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None
|
||||
343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None
|
||||
344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None
|
||||
345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
348,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
349,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
350,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
351,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
352,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
353,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
354,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
355,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
356,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
357,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
358,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
359,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,None
|
||||
360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None
|
||||
361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
364,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
365,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
366,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
367,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
368,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
369,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
370,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
371,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
372,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
373,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
374,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
|
||||
|
1310
shestakova_maria_lab_3/titanic.csv
Normal file
1310
shestakova_maria_lab_3/titanic.csv
Normal file
File diff suppressed because it is too large
Load Diff
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Reference in New Issue
Block a user